Realization of reservoir computing by delayed oscillators with active microparticles (Conference Presentation)

Xiangzun Wang; Frank Cichos

doi:10.1117/12.2633310

4 October 2022 Realization of reservoir computing by delayed oscillators with active microparticles (Conference Presentation)

Xiangzun Wang, Frank Cichos

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC1220417 (2022) https://doi.org/10.1117/12.2633310
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Reservoir computing as a highly efficient architecture for recurrent neural networks has been implemented in a variety of ways, including anharmonic oscillators, liquid surfaces, and optical and electronic circuits. Here, we investigate whether active particle networks that mimic fundamental dynamical processes of living systems can serve as reservoirs. In particular, we realize active particle oscillators, each consisting of an immobile and an active colloidal microparticle suspended in a layer of a liquid solution. The motion of the active particles is manipulated by a feedback system using a focused laser that stimulates the particles to float in 2D by thermophoresis [1]. The active particle is programmed to be attracted to the immobile particle with a delayed response that exhibits a pitchfork bifurcation, which introduces nonlinearity and memory into the response of a single active oscillator. Using time multiplexing, the propulsion of the oscillator is selected at different times as virtual nodes of a reservoir that are coupled to an input layer. Since the motion of the active particle is affected in a nonlinear manner with a memory of its previous state, the last node state is naturally coupled to the other nodes from different iterations due to the intrinsic property of the delayed oscillator. We illustrate the performance of the reservoir consisting of multiple oscillators with different delays by the tasks of nonlinear prediction and classification. [1]. F. Martin et al. ACS Nano 15, 2, 3434-3440 (2021)

Conference Presentation

Citation Download Citation

Xiangzun Wang and Frank Cichos "Realization of reservoir computing by delayed oscillators with active microparticles (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC1220417 (4 October 2022); https://doi.org/10.1117/12.2633310

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Oscillators

Particles

Liquids

Computer architecture

Electronic circuits

Multiplexing

Neural networks

Keywords/Phrases

Search In:

Publication Years